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Record W4410737949 · doi:10.1002/mame.202500130

Functional Polymers and Their Nanocomposites for Sustainable Packaging Applications

2025· article· en· W4410737949 on OpenAlexaff
Ritima Banerjee, Jayita Bandyopadhyay, Suprakas Sinha Ray

Bibliographic record

VenueMacromolecular Materials and Engineering · 2025
Typearticle
Languageen
FieldMaterials Science
Topicbiodegradable polymer synthesis and properties
Canadian institutionsUniversité Laval
FundersUniversity of JohannesburgDepartment of Science and Technology, Republic of South Africa
KeywordsMaterials scienceNanocompositePolymerPolymer nanocompositeNanotechnologyPolymer scienceComposite material

Abstract

fetched live from OpenAlex

Abstract This study explores recent advances and knowledge gaps in developing sustainable plastics‐based packaging materials, emphasizing functionality and nanotechnology's impact on sustainability. It discusses material selection decisions, such as replacing conventional materials or fossil‐based recyclable plastics with bio‐based biodegradable options, within cradle‐to‐grave life cycle assessments. The choice of end‐of‐life strategies, including recycling or biodegradation/composting, is influenced by existing infrastructure, providing realistic end‐of‐life scenario estimates. Other sustainability factors include extending shelf life, reducing food waste, minimizing material use, and enhancing recyclability. Evaluating economic viability and scalability is crucial for commercializing academic research, ensuring that these sustainable solutions are practical for society. Key attributes of the article highlight nanotechnology's role, functional improvements in sustainable packaging design, and the significance of life cycle assessment and economic feasibility for developing effective solutions. This review presents a comprehensive overview of essential factors for achieving sustainability and guiding the creation of high‐quality sustainable packaging for diverse markets.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.117
Threshold uncertainty score0.450

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.006
GPT teacher head0.184
Teacher spread0.179 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations8
Published2025
Admission routes1
Has abstractyes

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